- Consolidate memory search services by removing separate content_search.py and perceptual_search.py
- Update model client handling in base_pipeline.py to use ModelApiKeyService for LLM client initialization
- Add new prompt files and modify existing services to support consolidated search architecture
- Refactor memory read pipeline and related services to use updated model client approach
- Consolidate memory search services by removing separate content_search.py and perceptual_search.py
- Update model client handling in base_pipeline.py to use ModelApiKeyService for LLM client initialization
- Add new prompt files and modify existing services to support consolidated search architecture
- Refactor memory read pipeline and related services to use updated model client approach
When deleting an API Key, check if the associated model configuration has other active keys; if not, automatically set it to inactive.
Also optimize the model configuration query method to support multi-type queries and add sorting conditions.
- Skip alias merging for user entities during dedup (_merge_attribute and
_merge_entities_with_aliases) to prevent dirty data from overwriting
PgSQL authoritative aliases
- Add PgSQL→Neo4j alias sync after Neo4j write in write_tools to
ensure Neo4j user entities always reflect the PgSQL source
- Remove deduped_aliases (Neo4j history) from alias sync in
extraction_orchestrator, only append newly extracted aliases to PgSQL
- Guard Neo4j MERGE cypher to preserve existing aliases for user
entities (name IN ['用户','我','User','I'])
- Fix emotion_analytics_service query to use ExtractedEntity label
and entity_type property
Added `get_user_by_id_regardless_active` in user repository to support activation/deactivation workflows, updated `user_service` to use it, and refactored `_enrich_release_config` in `app_dsl_service` to accept `default_model_config_id` as a parameter instead of reading from config dict.
- Replace storage_services/search with new read_services/memory_search structure
- Implement content_search and perceptual_search strategies
- Add query_preprocessor for search optimization
- Create memory_service as unified interface
- Update celery_app and graph_search for new architecture
- Add enums for memory operations
- Implement base_pipeline and memory_read pipeline patterns
- Merge alias add/remove into MetadataExtractionResponse and Celery metadata task,
removing the separate sync step from extraction_orchestrator
- Replace first-person pronouns ("我") with "用户" in statement extraction to
preserve identity semantics for downstream metadata/alias extraction
- Update extract_statement.jinja2 prompt to enforce "用户" as subject for user
statements instead of resolving to real names
- Add alias change instructions (aliases_to_add/aliases_to_remove) to
extract_user_metadata.jinja2 with incremental merge logic
- Deduplicate special entities ("用户", "AI助手") in graph_saver by reusing
existing Neo4j node IDs per end_user_id
- Sync final aliases from PgSQL to Neo4j user entity nodes after metadata write
Create OpenClawTool class inheriting BuiltinTool with dedicated config
Remove all x-openclaw special handling from CustomTool (~270 lines)
Add multi-operation support (print_task, device_query, image_understand, general)
Change ensure_builtin_tools_initialized to incremental mode for auto-provisioning
Fix OperationTool and LangchainAdapter to support OpenClaw operation routing
- Add memory_config_id extraction and assignment when creating new end users in public share chat
- Introduce get_or_create_end_user_with_config method to handle memory config setup in single transaction
- Add batch_update_memory_config_id_by_app method for bulk updating end user memory configs
- Rename _update_endusers_memory_config_by_workspace to _update_endusers_memory_config_by_app for correct scope
- Update app publish flow to use app_id instead of workspace_id for memory config updates
- Remove unused actual_end_user_id variable in langchain_agent
- Ensures end users are properly associated with memory configs on creation and during app updates
- Migrate historical workflow queries from legacy ORM Query API to SQLAlchemy 2.0 select() + execute()
- Limit query fields and use pagination to reduce returned data, improving performance
- Preserve original ordering and filtering logic
